Inspired by the way that humans acquire new skills sequentially and improve over time, lifelong or continual learning  ([1]}, ) describes the goal of enabling AI systems to learn tasks sequentially over time while improving performance on both previous and future tasks. Lifelong learning has received much attention in the AI community, and many algorithms have been proposed for both supervised ([2]}) and reinforcement learning (). We include additional review of lifelong learning approaches in Appendix REF . A key challenge, apart from the learning algorithm itself, is the assessment of the learner: how well it works, where it fails, and what factors influence its success or failure. Many existing approaches focus on defining continual learning metrics, such as forgetting, backward transfer, and forward transfer  (, ); recently a suite of metrics for continual learning has been proposed . However, a metric such as forward transfer confounds three factors: the relationship between the tasks to be learned, the particular sequencing of tasks (curriculum), and the capabilities of the learner (). Use of systematic benchmarks (e.g.,  , ) can address part of this concern by fixing the tasks and curricula, but does not address how the learner might do on other (non-benchmark) tasks.
